Second, we introduce a new method for learning SSE based on a combination of ideas from representation learning. The model is a convolution and transformer-based embedder trained with the NTXEnt contrastive loss [1]}.
Building on similar ideas in vision and speech, we select our positive examples through a mix of time-stretching data augmentation [2]}
and k-Nearerst Neighbors search [3]}, [4]}. Figure REF  gives an overview of our method. To evaluate our method, we test our model on 5 types of acoustic features: MFCCs, CPC [5]},
HuBERT [6]} and Wav2Vec 2.0 (Base and Large) [7]}. We pick the best method from our LibriSpeech benchmark and show that when applied without any change to the task of spoken term discovery as defined in the zero resource challenges [8]}, we beat the state of the art on the NED/COV metric by a large margin in 5 new datasets.
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